MetaFunc: Taxonomic and Functional Analyses of High Throughput Sequencing for Microbiomes

Abstract

BackgroundThe identification of functional processes taking place in microbiome communities augment traditional microbiome taxonomic studies, giving a more complete picture of interactions taking place within the community. While there are applications that perform functional annotation on metagenome or metatranscriptomes, very few of these are able to link taxonomic identity to function and are limited by their input types or databases used.ResultsHere we present MetaFunc, a workflow which takes input reads, and from these 1) identifies species present in the microbiome sample and 2) provides gene ontology (GO) annotations associated with the species identified. MetaFunc can also provide a differential abundance analysis step comparing species between sample conditions. In addition, MetaFunc allows mapping of reads to a host genome, and separates these reads, before proceeding with the microbiome analyses. From the host reads, MetaFunc is able to identify host genes, perform differential gene expression analysis, and gene-set enrichment analysis. A final correlation analysis between microbial species and host genes can also be performed. Finally, MetaFunc builds an R shiny application that allows users to view and interact with the microbiome results. In this paper we show how MetaFunc can be applied to metatranscriptomic datasets of colorectal cancer.ConclusionMetaFunc is a one-stop shop microbiome analysis pipeline that can identify taxonomies and their respective functional contributions in a microbiome sample through GO annotations. It can also analyse host reads in a microbiome sample, providing information on host gene expression, and allowing for correlations between the microbiome and host genes. MetaFunc comes with a user-friendly R shiny application that allows for easier visualisation and exploration of its results. MetaFunc is freely available through https://gitlab.com/schmeierlab/workflows/metafunc.git.

Bacterial lipopolysaccharide modulates immune response in the colorectal tumor microenvironment

“…After trimming with SolexaQA++, the 260 samples were run through the MetaFunc pipeline (Sulit et al, 2020) using default settings, except for setting reverse stranded option for featureCounts (Liao et al, 2014), and a species needing at least 0.01% abundance in at least one of the 260 samples to be included in the microbiome analysis. Databases used were those provided in https://metafunc.readthedocs.io/en/latest/usage.html#databases.…”

Human Gene and Microbial Analyses Suggest Immunotherapy-like Mechanisms in Complete Response to Radiotherapy in Rectal Cancer

“…Raw sequencing data was parsed through the Metafunc pipeline (Sulit et al, 2020), which performs read pre-processing, host gene mapping, and microbiome species identification. Further details of the computational pipeline may be found at https://gitlab.com/schmeierlab/workflows/metafunc, and complete analysis of this paper is available at https://gitlab.com/alsulit08/uoc_response_rectalca.…”

“…Expression levels for each gene and sample were generated by the MetaFunc pipeline (Sulit et al, 2020), and DESeq2 (Love et al, 2014) was used to detect differentially expressed genes (DEGs). To detect DEGs that were significantly differentially expressed in the tumor relative to each participant's normal tissue between groups of responders the model fitted by DESeq2 included covariates for response (complete or other), tissue type (tumor or normal), response x participant (index) and response x tissue.…”

“…Raw sequencing data was parsed through the Metafunc pipeline (Sulit et al, 2020), which performs read pre-processing, host gene mapping, and microbiome species identification.…”